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2020 | OriginalPaper | Chapter

Assistive Technology Evolving as Intelligent System

Authors : Amlan Basu, Lykourgos Petropoulakis, Gaetano Di Caterina, John Soraghan

Published in: New Trends in Computational Vision and Bio-inspired Computing

Publisher: Springer International Publishing

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Abstract

Different evolving technologies surround humans today. Among the various technologies, Assistive Technology has still not established itself firmly because there is an absence of proper integration of this technology with human life. However, in the future, it will become one of the most important and vital phenomena in everyone’s life. Because humans want to make their life easier and longer and these are the reasons for the rapid growth in demand for Assistive Technology. Therefore, improvements in the technology and the way it is applied are essential and, for this reason, there is a requirement of a detailed study of the technology. This paper demonstrates the different milestones achieved in assistive technology by using different techniques to attempt to improve intelligence in assistive systems; and also, it describes the gaps that are still present even after such extensive works and, which are required to be either resolved or bridged. This study is done to understand where the assistive technology is today and in which direction it needs to get directed.

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Literature
1.
N. Harris, “The Design and Development of Assistive Technology,” IEEE Potentials, vol. 36, no. 1, pp. 24–28, 2017.CrossRef
2.
S. Russell, P. Norvig, and A. Intelligence, “A modern approach Artificial Intelligence,” Prentice-Hall, Englewood Cliffs, vol. 25, p. 27, 1995.
3.
V. Novák, I. Perfilieva, and J. Mockor, “Mathematical principles of fuzzy logic,” Springer Science & Business Media, vol. 517, 2012.
4.
C. W. De Silva, “Intelligent control: fuzzy logic applications,” CRC press, 2018.
5.
J. Sabatier, O. P. Agrawal, and J. A. T. Machado, “Advances in fractional calculus,” Springer, vol. 4, no. 9, 2007.
6.
D. Baleanu, J. A. T. Machado, and A. C. J. Luo, “Fractional dynamics and control,” Springer Science & Business Media, 2011.
7.
T. Li, G. Shao, W. Zuo, and S. Huang, “Genetic Algorithm for Building Optimization: State-of-the-Art Survey,” Proceedings of the 9th International Conference on Machine Learning and Computing. ACM, pp. 205–210, 2017.
8.
G. M. Khan, “Evolutionary computation,” Evolution of Artificial Neural Development: Springer, pp. 29–37, 2018.
9.
P. Jackson, “Introduction to expert systems,” Addison-Wesley Longman Publishing Co. Inc., 1998.
10.
J. McCarthy, “Some expert systems need common sense,” Annals of the New York Academy of Sciences, vol. 426, no. 1, pp. 129–137, 1984.CrossRef
11.
E. H. Shortliffe, R. Davis, S. G. Axline, B. G. Buchanan, C. C. Green, and S. N. Cohen, “Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system,” Computers and biomedical research, vol. 8, no. 4, pp. 303–320, 1975.CrossRef
12.
E. Shortliffe, “Computer-based medical consultations: MYCIN,” Elsevier, 2012.
13.
S. Liberatore, “Your AI lawyer will see you now: IBM’s ROSS becomes world’s first artificially intelligent attorney,” MailOnline-news, sport, celebrity, science and health stories, 2016.
14.
L. Luoren and L. Jinling, “Research of PID control algorithm based on neural network,” Energy Procedia, vol. 13, pp. 6988–6993, 2011.
15.
S. Rajasekaran and G. A. V. Pai, “Neural networks, fuzzy logic and genetic algorithm: synthesis and applications (with cd),” PHI Learning Pvt. Ltd., 2003.
16.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, p. 436, 2015.CrossRef
17.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.CrossRef
18.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, pp. 1097–1105, 2012.
19.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” European conference on computer vision. Springer, pp. 818–833, 2014.
20.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
21.
C. Szegedy, W. Liu, Y. Jia and P. Sermanet, “Going deeper with convolutions,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9, 2015.
22.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778, 2016.
23.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” European conference on computer vision. Springer, pp. 630–645, 2016.
24.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826, 2016.
25.
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” In AAAI, vol. 4, p. 12, 2017.
26.
A. Géron, “Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems,” Sebastopol, CA: O, 2017.
27.
M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science, vol. 349, no. 6245, pp. 255–260, 2015.MathSciNetCrossRef
28.
D. CireşAn, U. Meier, J. Masci, and J. Schmidhuber, “Multi-column deep neural network for traffic sign classification,” Neural Networks, vol. 32, pp. 333–338, 2012.CrossRef
29.
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition,” Neural networks, vol. 32, pp. 323–332, 2012.CrossRef
30.
M. Ali, “360 View Camera Based Visual Assistive Technology for Contextual Scene Information,” IEEE International Conference on Systems, Man, and Cybernetics, pp. 2135–2140, 2017.
31.
K. Chaccour and G. Badr, “Computer vision guidance system for indoor navigation of visually impaired people,” IEEE 8th International Conference on Intelligent Systems, 2016, pp. 449–454, 2016.
32.
D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.CrossRef
33.
Z. Zhang, “When doctors meet with AlphaGo: potential application of machine learning to clinical medicine,” Annals of translational medicine, vol. 4, no. 6, 2016.
34.
R. Girshick, “Fast r-cnn,” Proceedings of the IEEE international conference on computer vision, pp. 1440–1448, 2015.
35.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 6, pp. 1137–1149, 2017.
36.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440, 2015.
37.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” IEEE International Conference on Computer Vision, pp. 2980–2988, 2017.
38.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779–788, 2016.
39.
J. Redmon and A. Farhadi, “YOLO9000: better, faster, stronger,” arXiv preprint, vol. 1612, 2017.
40.
J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv preprint arXiv:1804.02767, 2018.
41.
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, “Places: A 10 million image database for scene recognition,” IEEE transactions on pattern analysis and machine intelligence, 2017.
42.
A. Quattoni and A. Torralba, “Recognizing indoor scenes,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 413–420, 2009.
43.
L. Wang, S. Guo, W. Huang, Y. Xiong, and Y. Qiao, “Knowledge guided disambiguation for large-scale scene classification with multi-resolution CNNs,” IEEE Transactions on Image Processing, vol. 26, no. 4, pp. 2055–2068, 2017.MathSciNetCrossRef
44.
G. E. Hinton, A. Krizhevsky, and S. D. Wang, “Transforming auto-encoders,” International Conference on Artificial Neural Networks. Springer, pp. 44–51, 2011.
45.
G. E. Hinton, S. Sabour, and N. Frosst, “Matrix capsules with EM routing,” 2018.
46.
S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between capsules,” Advances in Neural Information Processing Systems, pp. 3856–3866, 2017.
47.
W. Maass, “Networks of spiking neurons: the third generation of neural network models,” Neural networks, vol. 10, no. 9, pp. 1659–1671, 1997.CrossRef
48.
E. M. Izhikevich, “Simple model of spiking neurons,” IEEE Transactions on neural networks, vol. 14, no. 6, pp. 1569–1572, 2003.MathSciNetCrossRef
49.
A. Tavanaei, M. Ghodrati, S. R. Kheradpisheh, T. Masquelier, and A. S. Maida, “Deep Learning in Spiking Neural Networks,” arXiv preprint arXiv:1804.08150, 2018.
Metadata
Title
Assistive Technology Evolving as Intelligent System
Authors
Amlan Basu
Lykourgos Petropoulakis
Gaetano Di Caterina
John Soraghan
Copyright Year
2020
Publisher
Springer International Publishing
Book
New Trends in Computational Vision and Bio-inspired Computing

Print ISBN: 978-3-030-41861-8

Electronic ISBN: 978-3-030-41862-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-41862-5_27

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